Advertisement
Published in:
2009 | OriginalPaper | Chapter
Some Pairwise Constrained Semi-Supervised Fuzzy c-Means Clustering Algorithms
Authors : Yuchi Kanzawa, Yasunori Endo, Sadaaki Miyamoto
Published in: Modeling Decisions for Artificial Intelligence
Publisher: Springer Berlin Heidelberg
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
In this paper, some semi-supervised clustering methods are proposed with two types of pair constraints: two data have to be together in the same cluster, and two data have to be in different clusters, which are classified into two types: one is based on the standard fuzzy
c
-means algorithm and the other is on the entropy regularized one. First, the standard fuzzy
c
-means and the entropy regularized one are introduced. Second, a pairwise constrained semi-supervised fuzzy
c
means are introduced, which is derived from pairwise constrained competitive agglomeration. Third, some new optimization problem are proposed, which are derived from adding new loss function of memberships to the original optimization problem, respectively. Last, an iterative algorithm is proposed by solving the optimization problem.